System, method and article of manufacture for creating a filtered information summary based on multiple profiles of each single user6202062Abstract An agent based system assists in obtaining information from an article of interest and utilizes the information to take user directed action based on the information from the target article. The system obtains input text in character form indicative of the target meeting from the a calendar program that includes the time of the meeting. As the time of the meeting approaches, the calendar program is queried to obtain the text of the target event and that information is utilized as input to the agent system. Then, the agent system parses the input meeting text to extract its various components such as title, body, participants, location, time etc. The system also performs pattern matching to identify particular appropriate information. This information is utilized to query various sources of information on the web and obtain relevant stories about the current meeting to send back to the calendaring system. Claims What is claimed is: Description FIELD OF THE INVENTION
A.1.1.1.1.1 Public Type tMeetingRecord
sUserID As String (user id given by Munin)
sTitleOrig As String (original non stop listed title we need to
keep around to send back to Munin)
sTitleKW As String (stoplisted title with only keywords)
sBodyKW As String (stoplisted body with only keywords)
sCompany() As String (companys identified in title or body through
pattern matching)
sTopic() As String (topics identified in title or body through
pattern matching)
sPeople() As String (people identified in title or body through
pattern matching)
sWhen() As String (time identified in title or body through
pattern matching)
sWhere() As String (location identified in title or body through
pattern matching)
sLocation As String (location as passed in by Munin)
sTime As String (time as passed in by Munin)
sParticipants() As String (all participants engaged as passed in by
Munin)
sMeetingText As String (the original meeting text w/o userid)
End Type
There are two other structures which are created to hold each individual pattern utilized in pattern matching. The record tAPatternRecord is an array containing all the components/elements of a pattern. The type tAPatternElement is an array of strings which represent an element in a pattern. Because there may be many "substitutes" for each element, we need an array of strings to keep track of what all the substitutes are. The structures of tAPatternElement and tAPatternRecord are presented below in accordance with a preferred embodiment. Public Type tAPatternElement elementArray( ) As String End Type Public Type tAPatternRecord patternArray( ) As tAPatternElement End Type Common User Defined Constants Many constants are defined in each declaration section of the program which may need to be updated periodically as part of the process of maintaining the system in accordance with a preferred embodiment. The constants are accessible to allow dynamic configuration of the system to occur as updates for maintaining the code. Included in the following tables are lists of constants from each module which I thought are most likely to be modified from time to time. However, there are also other constants used in the code not included in the following list. It does not mean that these non-included constants will never be chanced. It means that they will chance much less frequently. For the Main Module (BF.Main):
CONSTANT PRESET VALUE USE
MSGTOMUNIN_TYPE 6 Define the message
number used to identify
messages between BF
and Munin
IP_ADDRESS_MUNIN "10.2.100.48" Define the IP address of
the machine in which
Munin and BF are
running on so they can
transfer data through
UDP.
PORT_MUNIN 7777 Define the remote port in
which we are operating
on.
TIMEOUT_AV 60 Define constants for
setting time out in inet
controls
TIMEOUT_NP 60 Define constants for
setting time out
in inet controls
CMD_SEPARATOR ".backslash." Define delimiter to tell
which part of Munin's
command represents the
beginning of our input
meeting text
OUTPARAM_SEPARAT "::" Define delimiter for
OR separating out different
portions of the output.
The separator is for
delimiting the msg
type, the user id, the
meeting title and the
beginning of the actual
stories retrieved.
For the Search Module (BF.Search):
CURRENT
CONSTANT VALUE USE
PAST_NDAYS 5 Define number of days you
want to look back for
AltaVista articles. Doesn't
really matter now because we
aren't really doing a news
search in alta vista. We want
all info.
CONNECTOR_AV_URL "+AND+" Define how to connect
keywords. We want all our
keywords in the string so
for now use AND. If you
want to do an OR or
something, just change
connector.
CONNECTOR_NP_URL "+AND+" Define how to connect
keywords. We want all our
keywords in the string so
for now use AND. If you
want to do an OR or
something, just change
connector.
NUM_NP_STORIES 3 Define the number of stories
to return back to Munin from
NewsPage.
NUM_AV_STORIES 3 Define the number of stories
to return back to Munin from
AltaVista.
For the Parse Module (BF.Parse):
CURRENT
CONSTANT VALUE USE
PORTION_SEPARATOR "::" Define the separator
between different
portions of the meeting
text sent in by Munin.
For example in
"09::Meet with
Chad::about
life::Chad.vertline.Denise::::::".
"::" is the
separator between
different parts of
the meeting text.
PARTICIPANT_SEPARATOR ".vertline." Define the separator
between each participant
in the participant list
portion of the original
meeting text.
Refer to example above.
For Pattern Matching, Module (BFPatternMatch): There are no constants in this module which require frequent updates. General Process Flow The best way to depict the process flow and the coordination of functions between each other is with the five flowcharts illustrated in FIGS. 2 to 6. FIG. 2 depicts the overall process flow in accordance with a preferred embodiment. Processing commences at the top of the chart at function block 200 which launches when the program starts. Once the application is started, the command line is parsed to remove the appropriate meeting text to initiate the target of the background find operation in accordance with a preferred embodiment as shown in function block 210. A global stop list is generated after the target is determined as shown in function block 220. Then, all the patterns that are utilized for matching operations are generated as illustrated in function block 230. Then, by tracing through the chart, function block 200 invokes GoBF 240 which is responsible for logical processing associated with wrapping the correct search query information for the particular target search engine. For example, function block 240 flows to function block 250 and it then calls GoPatternMatch as shown in function block 260. To see the process flow of GoPatternMatch, we swap to the diagram titled "Process Flow for BF's Pattern Matching Unit." One key thing to notice is that functions depicted at the same level of the chart are called by in sequential order from left to right (or top to bottom) by their common parent function. For example, Main 200 calls ProcessCommandLine 210, then CreateStopListist 220, then CreatePatterns 230, then GoBackgroundFinder 240. FIGS. 3 to 6 detail the logic for the entire program, the parsing unit, the pattern matching unit and the search unit respectively. FIG. 6 details the logic determinative of data flow of key information through BackgroundFinder, and shows the functions that are responsible for creating or processing such information. Detailed Search Architecture with Simple Query Mode Search Alta Vista Function Block 270 of FIG. 2 The Alta Vista search engine utilizes the identifies and returns general information about topics related to the current meeting as shown in function block 270 of FIG. 2. The system in accordance with a preferred embodiment takes all the keywords from the title portion of the original meeting text and constructs an advanced query to send to Alta Vista. The keywords are logically combined together in the query. The results are also ranked based on the same set of keywords. One of ordinary skill in the art will readily comprehend that a date restriction or publisher criteria could be facilitated on the articles we want to retrieve. A set of top ranking stories are returned to the calendaring system in accordance with a preferred embodiment. News Page Function Block 275 of FIG. 2 The NewsPage search system is responsible for giving us the latest news topics related to a target meeting. The system takes all of the keywords from the title portion of the original meeting text and constructs a query to send to the NewsPage search engine. The keywords are logically combined together in the query. Only articles published recently are retrieved. The Newspage search system provides a date restriction criteria that is settable by a user according to the user's preference. The top ranking stories are returned to the calendaring system. FIG. 3 is a user profile data model in accordance with a preferred embodiment. Processing commences at function block 300 which is responsible for invoking the program from the main module. Then, at function block 310, a wrapper function is invoked to prepare for the keyword extraction processing in function block 320. After the keywords are extracted, then processing flows to function block 330 to determine if the delimiters are properly positioned. Then, at function block 340, the number of words in a particular string is calculated and the delimiters for the particular field are and a particular field from the meeting text is retrieved at function block 350. Then, at function block 380, the delimiters of the string are again checked to assure they are placed appropriately. Finally, at function block 360, the extraction of each word from the title and body of the message is performed a word at a time utilizing the logic in function block 362 which finds the next closest word delimiter in the input phrase, function block 364 which strips unnecessary materials from a word and function block 366 which determines if a word is on the stop list and returns an error if the word is on the stop list. Pattern Matching in Accordance with a Preferred Embodiment The limitations associated with a simple searching method include the following: 1. Because it relies on a stoplist of unwanted words in order to extract from the meeting text a set of keywords, it is limited by how comprehensive the stoplist is. Instead of trying to figure out what parts of the meeting text we should throw away, we should focus on what parts of the meeting text we want. 2. A simple search method in accordance with a preferred embodiment only uses the keywords from a meeting title to form queries to send to Alta Vista and NewsPage. This ignores an alternative source of information for the query, the body of the meeting notice. We cannot include the keywords from the meeting body to form our queries because this often results in queries which are too long and so complex that we often obtain no meaningful results. 3. There is no way for us to tell what each keyword represents. For example, we may extract "Andy" and "Grove" as two keywords. However, a simplistic search has no way knowing that "Andy Grove" is in fact a person's name. Imagine the possibilities if we could somehow intelligently guess that "Andy Grove" is a person's name. We can find out if he is an Andersen person and if so what kind of projects he's been on before etc. etc. 4. In summary, by relying solely on a stoplist to parse out unnecessary words, we suffer from "information overload". Pattern Matching Overcomes these Limitations in Accordance with a Preferred Embodiment Here's how the pattern matching system can address each of the corresponding issues above in accordance with a preferred embodiment. 1. By doing pattern matching, we match up only parts of the meeting text that we want and extract those parts. 2. By performing pattern matching on the meeting body and extracting only the parts from the meeting body that we want. Our meeting body will not go to complete waste then. 3. Pattern matching is based on a set of templates that we specify, allowing us to identify people names, company names etc from a meeting text. 4. In summary, with pattern matching, we no longer suffer from information overload. Of course, the big problem is how well our pattern matching works. If we rely exclusively on artificial intelligence processing, we do not have a 100% hit rate. We are able to identify about 20% of all company names presented to us. Patterns A pattern in the context of a preferred embodiment is a template specifying the structure of a phrase we are looking for in a meeting text. The patterns supported by a preferred embodiment are selected because they are templates of phrases which have a high probability of appearing in someone's meeting text. For example, when entering a meeting in a calendar, many would write something such as "Meet with Bob Dutton from Stanford University next Tuesday." A common pattern would then be something like the word "with" followed by a person's name (in this example it is Bob Dutton) followed by the word "from" and ending with an organization's name (in this case, it is Stanford University). Pattern Matching Terminology The common terminology associated with pattern matching is provided below. Pattern: a pattern is a template specifying the structure of a phrase we want to bind the meeting text to. It contains sub units. p1 Element: a pattern can contain many sub-units. These subunits are called elements. For example, in the pattern "with $PEOPLE$ from $COMPANY$", "with" "$PEOPLE$" "from" "$COMPANY$" are all elements. Placeholder: a placeholder is a special kind of element in which we want to bind a value to. Using the above example, "$PEOPLE$" is a placeholder. Indicator: an indicator is another kind of element which we want to find in a meeting text but no value needs to bind to it. There may be often more than one indicator we are looking for in a certain pattern. That is why an indicator is not an "atomic" type. Substitute: substitutes are a set of indicators which are all synonyms of each other. Finding any one of them in the input is good. There are five fields which are identified for each meeting: Company ($COMPANY$) People ($PEOPLE$) Location ($LOCATION$) Time ($TIME$) Topic ($TOPIC_UPPER$) or ($TOPIC_ALL$) In parentheses are the placeholders I used in my code as representation of the corresponding meeting fields. Each placeholder has the following meaning: $COMPANY$: binds a string of capitalized words (e.g. Meet with Joe Carter of <Andersen Consulting>) $PEOPLE$: binds series of string of two capitalized words potentially connected by "," "and" or "&" (e.g. Meet with <Joe Carter> of Andersen Consulting, Meet with <Joe Carter and Luke Hughes> of Andersen Consulting) $LOCATION$: binds a string of capitalized words (e.g. Meet Susan at <Palo Alto Square>) $TIME$: binds a string containing the format #:## (e.g. Dinner at <6:30 pm>) $TOPIC_UPPER$: binds a string of capitalized words for our topic (e.g. <Stanford Engineering Recruiting> Meeting to talk about new hires). $TOPIC_ALL$: binds a string of words without really caring if it's capitalized or not. (e.g. Meet to talk about <ubiquitous computing>) Here is a table representing all the patterns supported by BF. Each pattern belongs to a pattern group. All patterns within a pattern group share a similar format and they only differ from each other in terms of what indicators are used as substitutes. Note that the patterns which are grayed out are also commented in the code. BF has the capability to support these patterns but we decided that matching these patterns is not essential at this point.
PAT PAT
GRP # PATTERN EXAMPLE
1 a $PEOPLE$ of Paul Maritz of Microsoft
$COMPANY$
b $PEOPLE$ from Bill Gates, Paul Allen and
$COMPANY$ Paul Maritz from Microsoft
2 a $TOPIC_UPPER$ meeting Push Technology Meeting
b $TOPIC_UPPER$ mtg Push Technology Mtg
c $TOPIC_UPPER$ demo Push Technology demo
d $TOPIC_UPPER$ Push Technology interview
interview
e $TOPIC_UPPER$ Push Technology
presentation presentation
f $TOPIC_UPPER$ visit Push Technology visit
g $TOPIC_UPPER$ briefing Push Technology briefing
h $TOPIC_UPPER$ Push Technology
discussion discussion
i $TOPIC_UPPER$ Push Technology
workshop workshop
j $TOPIC_UPPER$ prep Push Technology prep
k $TOPIC_UPPER$ review Push Technology review
l $TOPIC_UPPER$ lunch Push Technology lunch
m $TOPIC_UPPER$ project Push Technology project
n $TOPIC_UPPER$ projects Push Technology projects
3 a $COMPANY$ corporation Intel Corporation
b $COMPANY$ corp. IBM Corp.
c $COMPANY$ systems Cisco Systems
d $COMPANY$ limited IBM limited
e $COMPANY$ ltd IBM ltd
4 a about $TOPIC_ALL$ About intelligent agents
technology
b discuss $TOPIC_ALL$ Discuss intelligent agents
technology
c show $TOPIC_ALL$ Show the client our
intelligent agents
technology
d re: $TOPIC_ALL$ re: intelligent agents
technology
e review $TOPIC_ALL$ Review intelligent agents
technology
f agenda The agenda is as follows:
--clean up
--clean up
--clean up
g agenda: $TOPIC_ALL$ Agenda:
--demo client intelligent
agents technology.
--demo ecommerce.
5 a w/$PEOPLE$ of Meet w/Joe Carter of
$COMPANY$ Andersen Consulting
b w/$PEOPLE$ from Meet w/Joe Carter from
$COMPANY$ Andersen Consulting
6 a w/$COMPANY$ per Talk w/Intel per Jason
$PEOPLE$ Foster
7 a At $TIME$ at 3:00 pm
b Around $TIME$ Around 3:00 pm
8 a At $LOCATION$ At LuLu's resturant
b In $LOCATION$ in Santa Clara
9 a Per $PEOPLE$ per Susan Butler
10 a call w/$PEOPLE$ Conf call w/John Smith
B call with $PEOPLE$ Conf call with John Smith
11 A prep for $TOPIC_ALL$ Prep for London meeting
B preparation for Preparation for London
$TOPIC_ALL$ meeting
FIG. 4 is a detailed flowchart of pattern matching in accordance with a preferred embodiment. Processing commences at function block 400 where the main program invokes the pattern matching application and passes control to function block 410 to commence the pattern match processing. Then, at function block 420, the wrapper function loops through to process each pattern which includes determining if a part of the text string can be bound to a pattern as shown in function block 430. Then, at function block 440, various placeholders are bound to values if they exist, and in function block 441, a list of names separated by punctuation are bound, and at function block 442 a full name is processed by finding two capitalized words as a full name and grabbing the next letter after a space after a word to determine if it is capitalized. Then, at function block 443, time is parsed out of the string in an appropriate manner and the next word after a blank space in function block 444. Then, at function block 445, the continuous phrases of capitalized words such as company, topic or location are bound and in function block 446, the next word after the blank is obtained for further processing in accordance with a preferred embodiment. Following the match meeting field processing, function block 450 is utilized to loacte an indicator which is the head of a pattern, the next word after the blank is obtained as shown in function block 452 and the word is checked to determine if the word is an indicator as shown in function block 454. Then, at function block 460, the string is parsed to locate an indicator which is not at the end of the pattern and the next word after unnecessary white space such as that following a line feed or a carriage return is processed as shown in function block 462 and the word is analyzed to determine if it is an indicator as shown in function block 464. Then, in function block 470, the temporary record is reset to the null set to prepare it for processing the next string and at function block 480, the meeting record is updated and at function block 482 a check is performed to determine if an entry is already made to the meeting record before parsing the meeting record again. Using the Identified Meeting Fields Now that we have identified fields within the meeting text which we consider important, there are quite a few things we can do with it. One of the most important applications of pattern matching is of course to improve the query we construct which eventually gets submitted to Alta Vista and News Page. There are also a lot of other options and enhancements which exploit the results of pattern matching that we can add to BF. These other options will be described in the next section. The goal of this section is to give the reader a good sense of how the results obtained from pattern matching can be used to help us obtain better search results. FIG. 5 is a flowchart of the detailed processing for preparing a query and obtaining information from the Internet in accordance with a preferred embodiment. Processing commences at function block 500 and immediately flows to function block 510 to process the wrapper functionality to prepare for an Internet search utilizing a web search engine. If the search is to utilize the Alta Vista search engine, then at function block 530, the system takes information from the meeting record and forms a query in function blocks 540 to 560 for submittal to the search engine. If the search is to utilize the NewsPage search engine, then at function block 520, the system takes information from the meeting record and forms a query in function blocks 521 to 528. Alta Vista Search Engine The strength of the Alta Vista search engine is that it provides enhanced flexibility. Using its advance query method, one can construct all sorts of Boolean queries and rank the search however you want. However, one of the biggest drawbacks with Alta Vista is that it is not very good at handling a large query and is likely to give back irrelevant results. If we can identify the topic and the company within a meeting text, we can form a pretty short but comprehensive query which will hopefully yield better results. We also want to focus on the topics found. It may not be of much merit to the user to find out info about a company especially if the user already knows the company well and has had numerous meetings with them. It's the topics they want to research on. News Page Search Engine The strength of the News Page search engine is that it does a great job searching for the most recent news if you are able to give it a valid company name. Therefore when we submit a query to the news page web site, we send whatever company name we can identify and only if we cannot find one do we use the topics found to form a query. If neither one is found, then no search is performed. The algorithm utilized to form the query to submit to Alta Vista is illustrated in FIG. 7. The algorithm that we will use to form the query to submit to News Page is illustrated in FIG. 8. The following table describes in detail each function in accordance with a preferred embodiment. The order in which functions appear mimics the process flow as closely as possible. When there are situations in which a function is called several times, this function will be listed after the first function which calls it and its description is not duplicated after every subsequent function which calls it.
Procedure
Name Type Called By Description
Main Public None This is the main function
(BF.Main) Sub where the program first
launches. It initializes BF
with the appropriate
parameters(e.g. Internet time-
out, stoplist...) and calls
GoBF to launch the main
part of the program.
ProcessCom Private Main This function parses the
mandLine Sub command line. It assumes
(BF.Main) that the delimiter indicating
the beginning of input from
Munin is stored in the
constant
CMD_SEPARATOR.
CreateStopLi Private Main This function sets up a stop
st Function list for future use to parse
out
(BF.Main) unwanted words from the
meeting text.
There are commas on each
side of each word to enable
straight checking.
CreatePattern Public Main This procedure is called once
s Sub when BF is first initialized to
(BF.Pattern create all the potential
Match) patterns that portions of the
meeting text can bind to. A
pattern can contain however
many elements as needed.
There are
two types of elements. The
first type of elements are
indicators. These are real
words which delimit the
potential of a meeting field
(eg company) to follow.
Most of these indicators are
stop words as expected
because
stop words are words
usually common to all
meeting text so it makes
sense they form patterns. The
second type of elements are
special strings which
represent placeholders.
A placeholder is always in
the form of $*$ where * can
be either PEOPLE,
COMPANY,
TOPIC_UPPER,
TIME,LOCATION or
TOPIC_ALL. A pattern can
begin with either one of the
two types of elements and
can be however long,
involving however any
number/type of elements.
This procedure dynamically
creates a new pattern record
for
each pattern in the table and
it also dynamically creates
new tAPatternElements for
each element within a
pattern. In addition, there is
the concept of being able to
substitute indicators within a
pattern. For example, the
pattern $PEOPLE$ of
$COMPANY$ is similar to
the pattern $PEOPLE$ from
$COMPANY$. "from" is a
substitute for "of". Our
structure should be able to
express such a need for
substitution.
GoBF Public Main This is a wrapper procedurer
(BF.Main) Sub that calls both the parsing
and the searching subroutines
of the
BF. It is also responsible for
sending data back to Munin.
ParseMeeting Public GoBackGroundF This function takes the initial
Text Function inder meeting text and identifies
(BF.Parse) the userID of the record as
well as other parts of the
meeting text including the
title, body, participant list,
location and time. In
addition, we call a helper
function ProcessStopList to
eliminate all the unwanted
words from the original
meeting title and meeting
body so that only keywords
are left. The information
parsed out is stored in the
MeetingRecord structure.
Note that this function does
no error checking and for the
most time assumes that the
meeting text string is
correctly formatted by
Munin.
The important variable is
thisMeeting Record is the
temp holder for all info
regarding current meeting.
It's eventually returned to
caller.
FormatDelim Private ParseMeetingTe There are 4 ways in which
itation xt, the delimiters can be placed.
(BF.Parse) DetermineNum We take care of all these
Words, cases by reducing them
GetAWordFrom down to Case 4 in which
String there are no delimiters
around but only between
fields in a string(e.g.
A::B::C)
DetermineNu Public ParseMeeting This functions determines
mWords Function Text, how many words there are in
(BF.Parse) ProcessStop a string (stInEvalString) The
List function assumes that each
word is separated by a
designated separator as
specified in stSeparator. The
return type is an integer that
indicates how many words
have been found assuming
each word
in the string is separated by
stSeparator. This function is
always used along with
GetAWordFromString and
should be called before
calling GetAWordFrom
String.
GetAWordFr Public ParseMeeting This function extracts the ith
omstring Function Text, word of the
(BF.Parse) ProcessStop string(stInEvalString)
List assuming that each word in
the string is separated by a
designated
separator contained in the
variable stSeparator.
In most cases, use this
function with
DetermineNumWords. The
function returns the wanted
word. This function checks
to make sure that
iInWordNum is within
bounds so that i
is not greater than the total
number of words in string or
less than/equal to zero. If it
is out of bounds, we return
empty string to indicate we
can't get anything. We try to
make sure this doesn't
happen by calling
DetermineNumWords first.
ParseAndCle Private ParseMeetingTe This function first grabs the
anPhrase Function xt word and send it to
(BF.Parse) CleanWord in order strip
the stuff that nobody wants.
There are things in
parseWord that will kill
the word, so we will need a
method of looping through
the body and rejecting
words without killing the
whole function
i guess keep CleanWord and
check a return value
ok, now I have a word so I
need to send it down the
parse chain. This chain goes
ParseCleanPhrase ->
CleanWord ->
EvaluateWord. If the word
gets through the
entire chain without being
killed, it will be added at the
end to our keyword string.
first would be the function
that checks for "/" as a
delimiter and extracts the
parts of that. This I will call
"StitchFace" (Denise is more
normal and calls it
GetAWordFromString)
if this finds words, then each
of these will be sent, in turn,
down the chain. If
these get through the entire
chain without being added or
killed then they will be
added rather than tossed.
FindMin Private ParseAndCleanP This function takes in 6 input
(BF.Parse) Function hrase values and evaluates to see
what the minimum non
zero value is. It first creates
an array as a holder so that
we can sort the five
input values in ascending
order. Thus the minimum
value will be the first non
zero value element of the
array. If we go through
entire array without finding
a non zero value, we know
that there is an error and we
exit the function.
CleanWord Private ParseAndCleanP This function tries to clean
(BF.Parse) Function hrase up a word in a meeting text.
It first of all determines if
the
string is of a valid length. It
then passes it through a
series of tests to see it is
clean and when needed, it
FIG. 6 is a flowchart of the actual code utilized to prepare and submit searches to the Alta Vista and Newspage search engines in accordance with a preferred embodiment. Processing commences at function block 610 where a command line is utilized to update a calendar entry with specific calendar information. The message is next posted in accordance with function block 620 and a meeting record is created to store the current meeting information in accordance with function block 630. Then, in function block 640 the query is submitted to the Alta Vista search engine and in function block 650, the query is submitted to the Newspage search engine. When a message is returned from the search engine, it is stored in a results data structure as shown in function block 660 and the information is processed and stored in summary form in a file for use in preparation for the meeting as detailed in function block 670. FIG. 7 provides more detail on creating the query in accordance with a preferred embodiment. Processing commences at function block 710 where the meeting record is parsed to obtain potential companies, people, topics, location and a time. Then, in function block 720, at least one topic is identified and in function block 720, at least one company name is identified and finally in function block 740, a decision is made on what material to transmit to the file for ultimate consumption by the user. FIG. 8 is a variation on the query theme presented in FIG. 7. A meeting record is parsed in function block 800, a company is identified in function block 820, a topic is identified in function block 830 and finally in function block 840 the topic and or the company is utilized in formulating the query. Alternative embodiments for adding various specific features for specific user requirements are discussed below. Enhance Target Rate for Pattern Matching To increase BF's performance, more patterns/pattern groups are added to the procedure "CreatePatterns." The existing code for declaring patterns can be used as a template for future patterns. Because everything is stored as dynamic arrays, it is convenient to reuse code by cutting and pasting. The functions BindName, BindTime, BindCompanyLocTopic which are responsible for associating a value with a placeholder can be enhanced. The enhancement is realized by increasing the set of criteria for binding a certain meeting field in order to increase the number of binding values. For example, BindTime currently accepts and binds all values in the form of ##:## or #:##. To increase the times we can bind, we may want BindTime to also accept the numbers 1 to 12 followed by the more aesthetic time terminology "o'clock." Vocabulary based recognition algorithms and assigning an accuracy rate to each guess BF makes allowing only guesses which meet a certain threshold to be valid. Depending on what location the system identifies through pattern matching or alternatively depending on what location the user indicates as the meeting place, a system in accordance with a preferred embodiment suggests a plurality of fine restaurants whenever it detects the words lunch/dinner/breakfast. We can also use a site like company finder to confirm what we got is indeed a company name or if there is no company name that pattern matching can identify, we can use a company finder web site as a "dictionary" for us to determine whether certain capitalized words represent a company name. We can even display stock prices and breaking news for a company that we have identified. Wireless Bargain Identification in Accordance with a Preferred Embodiment FIG. 9 is a flow diagram that depicts the hardware and logical flow of control for a device and a software system designed to allow Web-based comparison shopping in conventional, physical, non-Web retail environments. A wireless phone or similar hand-held wireless device 920 with Internet Protocol capability is combined with a miniature barcode reader 910 (installed either inside the phone or on a short cable) and used to scan the Universal Product Code (UPC) bar code on a book or other product 900. The wireless device 920 transmits the bar code via an antennae 930 to the Pocket BargainFinder Service Module (running on a Web server) 940, which converts it to (in the case of books) its International Standard Book Number or (in the case of other products) whatever identifier is appropriate. The Service Module then contacts the appropriate third-party Web site(s) to find price, shipping and availability information on the product from various Web suppliers 950. This information is formatted and displayed on the hand-held device's screen. The IP wireless phone or other hand held device 920 utilizes a wireless modem such as a Ricochet SE Wireless Modem from Metricom. Utilizing this device, a user can hang out in a coffee shop with a portable computer perched on a rickety little table, with a latte sloshing dangerously close to the keyboard, and access the Internet at speeds rivaling direct connect via a telephone line. The 8-ounce Ricochet SE Wireless Modem is about as large as a pack of cigarettes and setup is extremely simple, simply attach the modem to the back of your portable's screen with the included piece of Velcro, plug the cable into the serial port, flip up the stubby antenna, and transmit. Software setup is equally easy: a straightforward installer adds the Ricochet modem drivers and places the connection icon on your desktop. The functional aspects of the modem are identical to that of a traditional telephone modem. Of course, wireless performance isn't nearly as reliable as a traditional dial-up phone connection. We were able to get strong connections in several San Francisco locations as long as we stayed near the windows. But inside CNET's all-brick headquarters, the Ricochet couldn't connect at all. When you do get online, performance of up to 28.8 kbps is available with graceful degradation to slower speeds. But even the slower speeds didn't disappoint. Compared to the alternative--connecting via a cellular modem--the Ricochet is much faster, more reliable, and less expensive to use. Naturally, the SE Wireless is battery powered. The modem has continuous battery life of up to 12 hours. And in accordance with a preferred embodiment, we ran down our portable computer's dual cells before the Ricochet started to fade. Thus, utilizing the wireless modem, a user may utilize the web server software 940 to identify the right product 950 and then use an appropriate device's key(s) to select a supplier and place an order in accordance with a preferred embodiment. The BargainFinder Service Module then consummates the order with the appropriate third-party Web supplier 960. mySite! Personal Web Site & Intention Value Network Prototype mySite! is a high-impact, Internet-based application in accordance with a preferred embodiment that is focused on the theme of delivering services and providing a personalized experience for each customer via a personal web site in a buyer-centric world. The services are intuitively organized around satisfying customer intentions - fundamental life needs or objectives that require extensive planning decisions, and coordination across several dimensions, such as financial planning, healthcare, personal and professional development, family life, and other concerns. Each member owns and maintains his own profile, enabling him to create and browse content in the system targeted specifically at him. From the time a demand for products or services is entered, to the completion of payment, intelligent agents are utilized to conduct research, execute transactions and provide advice. By using advanced profiling and filtering, the intelligent agents learn about the user, improving the services they deliver. Customer intentions demonstrated include Managing Daily Logistics (e.g., email, calendar, contacts, to-do list, bill payment, shopping, and travel planning); and Moving to a New Community (e.g., finding a place to live, moving household possessions, getting travel and shipping insurance coverage, notifying business and personal contacts, learning about the new community). From a consumer standpoint, mySite! provides a central location where a user can access relevant products and services and accomplish daily tasks with ultimate ease and convenience. From a business standpoint, mySite! represents a value-added and innovative way to effectively attract, service, and retain customers. Egocentric Interface An Egocentric Interface is a user interface crafted to satisfy a particular user's needs, preferences and current context. It utilizes the user's personal information that is stored in a central profile database to customize the interface. The user can set security permissions on and preferences for interface elements and content. The content integrated into the Egocentric Interface is customized with related information about the user. When displaying content, the Egocentric Interface will include the relationship between that content and the user in a way that demonstrates how the content relates to the user. For instance, when displaying information about an upcoming ski trip the user has signed up for, the interface will include information about events from the user's personal calendar and contact list, such as other people who will be in the area during the ski trip. This serves to put the new piece of information into a context familiar to the individual user. FIG. 10B is a flowchart providing the logic utilized to create a web page within the Egocentric Interface. The environment assumes a web server and a web browser connected through a TCP/IP network, such as over the public Internet or a private Intranet. Possible web servers could include Microsoft Internet Information Server, Netscape Enterprise Server or Apache. Possible web browsers include Microsoft Internet Explorer or Netscape Navigator. The client (i.e. web browser) makes a request 1001 to the server (i.e. web server) for a particular web page. This is usually accomplished by a user clicking on a button or a link within a web page. The web server gets the layout and content preferences 1002 for that particular user, with the request to the database keyed off of a unique user id stored in the client (i.e. web browser) and the User profile database 1003. The web server then retrieves the content 1004 for the page that has been requested from the content database 1005. The relevant user-centric content, such as calendar, email, contact list, and task list items are then retrieved 1006. (See FIG. 11 for a more detailed description of this process.) The query to the database utilizes the user content preferences stored as part of the user profile in the User profile database 1003 to filter the content that is returned. The content that is returned is then formatted into a web page 1007 according to the layout preferences defined in the user profile. The web page is then returned to the client and displayed to the user 1008. FIG. 11 describes the process of retrieving user-centric content to add to a web page. This process describes 1006 in FIG. 10B in a more detailed fashion. It assumes that the server already has obtained the user profile and the existing content that is going to be integrated into this page. The server parses 1110 the filtered content, looking for instances of events, contact names and email addresses. If any of these are found, they are tagged and stored in a temporary holding space. Then, the server tries to find any user-centric content 1120 stored in various databases. This involves matching the tagged items in the temporary storage space with calendar items 1130 in the Calendar Database 1140; email items 1115 in the Email Database 1114; contact items 1117 in the Contact Database 1168; task list items 1119 in the Task List Database 1118; and news items 1121 in the News Database 1120. After retrieving any relevant user-centric content, it is compiled together and returned 1122. User Persona The system allows the user to create a number of different personas that aggregate profile information into sets that are useful in different contexts. A user may create one persona when making purchases for his home. This persona may contain his home address and may indicate that this user is looking to find a good bargain when shopping. The same user may create a second persona that can be used when he is in a work context. This persona may store the user's work address and may indicate that the user prefers certain vendors or works for a certain company that has a discount program in place. When shopping for work-related items, the user may use this persona. A persona may also contain rules and restrictions. For instance, the work persona may restrict the user to making airline reservations with only one travel agent and utilizing booking rules set up by his employer. FIG. 12 describes the relationship between a user, his multiple personas and his multiple profiles. At the User Level is the User Profile 1200. This profile describes the user and his account information. There is one unique record in the database for each user who has an account. Attached to each us | ||||||